Adaptable stowage elements

ABSTRACT

A current location and a destination location of a vehicle are identified. A stowage parameter is predicted based on the current and destination locations. A vehicle component is actuated based on the stowage parameter.

BACKGROUND

Vehicles can transport users and cargo to destinations. Upon arriving atthe vehicle, a user may possess an object, such as luggage, that needsto be stored in the vehicle during transport. However, a vehicle may nothave room for an object and/or a practical place to stow the object.Problems with current object stowage and transport technology include alack of ability to predict object stowage needs and/or to accommodatestowage of various objects to be transported in a vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system for predicting stowageparameters in a vehicle.

FIG. 2 is a perspective view of example stowage elements in an examplevehicle.

FIG. 3 is perspective view of other example stowage elements in anexample vehicle.

FIG. 4 is an example process for predicting stowage parameters of avehicle.

DETAILED DESCRIPTION

A system includes a computer programmed to identify a current locationand a destination of a vehicle, predict a stowage parameter based on thecurrent and destination locations, and actuate a vehicle component basedon the stowage parameter.

The vehicle component can be one of a suspension, a shelf, a hook, astorage bin, and a seat.

The computer can be further programmed to predict the stowage parameterbased on a characteristic of an object as well as one or both of thecurrent and destination locations in the vehicle. The computer can befurther programmed to predict one stowage parameter for each object of aplurality of objects.

The computer can be further programmed to receive a message from a userdevice and predict the stowage parameter based on the message from theuser device. The computer can be further programmed to predict thestowage parameter based on a vehicle activity log. The computer can befurther programmed to predict the stowage parameter based on a messagefrom each sensor in a set of seat sensors. The computer can be furtherprogrammed to predict the stowage parameter by applying rules derivedfrom machine learning.

A system includes a vehicle stowage element having an actuator arrangedto move at least part of the stowage element, and a computer programmedto identify a current location and a destination of a vehicle, predict astowage parameter based on the current and destination locations, andactuate a vehicle component based on the stowage parameter.

The computer can be further programmed to predict the stowage parameterbased on a characteristic of an object as well as one or both of thecurrent and destination locations in the vehicle.

A method includes identifying a current location and a destinationlocation of a vehicle, predicting a stowage parameter based on thecurrent and destination locations, and actuating a vehicle componentbased on the stowage parameter.

The vehicle component can be one of a suspension, a shelf, a hook, astorage bin, and a seat.

The method can further include predicting the stowage parameter based ona characteristic of an object as well as one or both of the current anddestination locations in the vehicle. The method can further includepredicting one stowage parameter for each object of a plurality ofobjects.

The method can further include receiving a message from a user deviceand predict the stowage parameter based on the message from the userdevice. The method can further include predicting the stowage parameterbased on a vehicle activity log. The method can further includepredicting the stowage parameter based on a message from each sensor ina set of seat sensors. The method can further include predicting thestowage parameter by applying rules derived from machine learning.

Further disclosed is a computing device programmed to execute any of theabove method steps. Yet further disclosed is a vehicle comprising thecomputing device. Yet further disclosed is a computer program product,comprising a computer readable medium storing instructions executable bya computer processor, to execute any of the above method steps.

FIG. 1 illustrates an example system 100, including a computer 105programmed to identify a current location and a destination location ofa vehicle 101, and predict a stowage parameter based on the current anddestination locations of the vehicle 101. A stowage parameter in thecontext of this disclosure is a value or rule specifying a manner inwhich objects at a location can be stored or stowed, e.g., for transportbetween a current location and a destination location, in a vehicle 101.The computer 105 can maintain lists of stowage parameters in the vehicle101 based on vehicle 101 locations and objects stowed in the vehicle101. The computer 105 can maintain a list of possible stowage parametersaccording to substantially unique identifiers for each parameter and/ordescriptors (e.g., “hang,” “enclose,” “support,” etc.), along with a setof coordinates specifying a vehicle 101 location associated with eachrespective parameter. The computer 105 can store a set geo-coordinatesindicating a vehicle 101 location and/or can store an identifier for theobject that can likewise be associated with a stowage parameter. Basedon the vehicle 101 location and the object, the computer 105 candetermine the stowage parameter required at a specific location. Thecomputer 105 can then actuate one or more vehicle 101 components basedon the stowage parameter, e.g., to navigate the vehicle 101 to thedestination location.

A computer 105 in the vehicle 101 is programmed to receive collecteddata 115 from one or more sensors 110. For example, vehicle 101 data 115may include a location of the vehicle 101, a location of a target, etc.Location data may be in a known form, e.g., geo-coordinates such aslatitude and longitude coordinates obtained via a navigation system, asis known, that uses the Global Positioning System (GPS). The navigationsystem can continuously monitor the location data 115 for the currentlocation of the vehicle 101. The user can input the location data 115for a destination location into the navigation system, e.g., to receivedirections to the destination location. Further examples of data 115 caninclude measurements of vehicle 101 systems and components, e.g., avehicle 101 velocity, a vehicle 101 trajectory, etc.

The computer 105 is generally programmed for communications on a vehicle101 network, e.g., including a communications bus, as is known. Via thenetwork, bus, and/or other wired or wireless mechanisms (e.g., a wiredor wireless local area network in the vehicle 101), the computer 105 maytransmit messages to various devices in a vehicle 101 and/or receivemessages from the various devices, e.g., controllers, actuators,sensors, etc., including sensors 110. Alternatively, or additionally, incases where the computer 105 actually comprises multiple devices, thevehicle network may be used for communications between devicesrepresented as the computer 105 in this disclosure. In addition, thecomputer 105 may be programmed for communicating with the network 125,which, as described below, may include various wired and/or wirelessnetworking technologies, e.g., cellular, Bluetooth®, Bluetooth® LowEnergy (BLE), wired and/or wireless packet networks, etc.

The data store 106 may be of any known type, e.g., hard disk drives,solid state drives, servers, or any volatile or non-volatile media. Thedata store 106 may store the collected data 115 sent from the sensors110.

Sensors 110 may include a variety of devices. For example, as is known,various controllers in a vehicle 101 may operate as sensors 110 toprovide data 115 via the vehicle 101 network or bus, e.g., data 115relating to vehicle speed, acceleration, position, subsystem and/orcomponent status, etc. Further, other sensors 110 could include cameras,motion detectors, etc., i.e., sensors 110 to provide data 115 forevaluating a location of a target, projecting a path of a target,evaluating a location of a roadway lane, etc. The sensors 110 could alsoinclude short range radar, long range radar, LIDAR, and/or ultrasonictransducers.

Collected data 115 may include a variety of data collected in a vehicle101. Examples of collected data 115 are provided above, and moreover,data 115 are generally collected using one or more sensors 110, and mayadditionally include data calculated therefrom in the computer 105,and/or at the server 130. In general, collected data 115 may include anydata that may be gathered by the sensors 110 and/or computed from suchdata.

The vehicle 101 may include a plurality of vehicle components 120. Asused herein, each vehicle component 120 includes one or more hardwarecomponents adapted to perform a mechanical function or operation—such asmoving the vehicle 101, slowing or stopping the vehicle 101, steeringthe vehicle 101, etc. Non-limiting examples of components 120 include apropulsion component (that includes, e.g., an internal combustion engineand/or an electric motor, etc.), a transmission component, a steeringcomponent (e.g., that may include one or more of a steering wheel, asteering rack, etc.), a brake component, a park assist component, anadaptive cruise control component, an adaptive steering component,stowage elements, etc.

The vehicle 101 can include a suspension component. The suspensioncomponent controls the height of a vehicle 101 body relative to adriving surface. For example, the suspension component can includesprings, shocks, etc. to maintain a consistent height of the vehicle 101body while the vehicle 101 is in transit. The suspension component canbe adjusted to change the height of the vehicle 101 body. For example,the suspension component can be actuated between a driving position anda stowage position. When the suspension component is in the drivingposition, the vehicle 101 body is farther from the driving surface thancompared to when the suspension component is in the stowage position.The suspension component can be in the driving position, e.g., when thevehicle 101 is in transit. The suspension component can be in thestowage position, e.g., at a current location and/or a destinationlocation, to assist with the stowage of an object. The computer 105 canactuate the suspension component from the driving position to thestowage position based on the stowage parameters.

The vehicle 101 can include a human-machine interface (HMI) 120, e.g.,one or more of a display, a touchscreen display, a microphone, aspeaker, etc. The user can input data 115 into the HMI 120, e.g., thecurrent location of the vehicle 101, the destination location of thevehicle 101, an object to be stowed at one of the current anddestination locations, etc. For example, the user can input the objectdata 115, e.g., object characteristics, an identifier associated withthe object, and image of the object, etc., into the HMI 120 and thecomputer 105 can determine the stowage parameter for stowing the object.As another example, the user can input location data 115, e.g., adestination location, into the HMI 120, and the computer 105 candetermine the stowage parameters for stowing objects associated withe.g., acquirable at, the destination location. The HMI 120 cancommunicate with the computer 105 via the vehicle 101 network, e.g., theHMI 120 can send a message including the user input, e.g., the locationdata 115 and/or the object data 115, to the computer 105. The computer105 can determine the stowage parameters based on the message from theHMI 120.

The vehicle 101 includes a plurality of seats 120. The seats 120 cansupport users in the vehicle 101 cabin. The seats 120 can be arranged inthe vehicle 101 cabin to accommodate users and objects, e.g., luggage.The seats 120 can be folded from a seated position to a stowageposition. In the seated position in one example, a seatback can extendupwardly relative to a seat bottom, e.g., the seat can support a user ina conventional sitting position. Continuing this example, in the stowageposition, the seatback can extend substantially parallel to the seatbottom, e.g., the seatback can lay across the seat bottom, and cansupport objects stowed in the vehicle 101.

Each seat 120 can include a seat sensor 110. The seat sensor 110 candetect the presence of a user sitting on the seat 120. The seat sensor110 can send data 115 to the computer 105, and the computer 105 candetermine whether a user is present in the seat 120. The computer 105can compare the data 115 from the seat sensor 110 to a threshold. Whenthe data 115 from the user detection sensor 110 exceeds the threshold,the computer 105 can determine that a user is in the seat 120. Forexample, if the seat sensor 110 is a weight sensor, the computer 105 cancompare collected user weight data 115 to a weight threshold. When theuser weight data 115 exceed the weight threshold, the computer 105 candetermine that the user is present in the seat 120. When the data 115from the seat sensor 110 is below the threshold (e.g., the user weightdata 115 are below the weight threshold), the computer 105 can determinethat the user is not in the seat 120.

When the computer 105 operates the vehicle 101, the vehicle 101 is an“autonomous” vehicle 101. For purposes of this disclosure, the term“autonomous vehicle” is used to refer to a vehicle 101 operating in afully autonomous mode. A fully autonomous mode is defined as one inwhich each of vehicle 101 propulsion (typically via a powertrainincluding an electric motor and/or internal combustion engine), braking,and steering are controlled by the computer 105. A semi-autonomous modeis one in which at least one of vehicle 101 propulsion (typically via apowertrain including an electric motor and/or internal combustionengine), braking, and steering are controlled at least partly by thecomputer 105 as opposed to a human operator.

The system 100 may further include a network 125 providingcommunications between other devices, e.g., a server 130 and a datastore 135. The network 125 represents one or more mechanisms by which avehicle computer 105 may communicate with a remote server 130.Accordingly, the network 125 may be one or more of various wired orwireless communication mechanisms, including any desired combination ofwired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless,satellite, microwave, and radio frequency) communication mechanisms andany desired network topology (or topologies when multiple communicationmechanisms are utilized). Exemplary communication networks includewireless communication networks (e.g., using Bluetooth®, BLE, IEEE802.11, vehicle-to-vehicle (V2V) such as Dedicated Short RangeCommunications (DSRC), etc.), local area networks (LAN) and/or wide areanetworks (WAN), including the Internet, providing data communicationservices.

The system 100 may include a user device 140. As used herein, a “userdevice” is a portable, computing device that includes a memory, aprocessor, a display, and one or more input mechanisms, such as atouchscreen, buttons, etc., as well as hardware and software forwireless communications such as described herein. Accordingly, the userdevice 140 may be any one of a variety of computing devices including aprocessor and a memory, e.g., a smartphone, a tablet, a personal digitalassistant, etc. The user device 140 may use the network 125 tocommunicate with the vehicle computer 105. For example, the user device140 can be communicatively coupled to each other and/or to the vehiclecomputer 105 with wireless technologies such as described above. Theuser device 140 includes a user device processor 145.

The computer 105 can predict the stowage parameters based on the objectdata 115 and/or the location data 115. The computer 105 can receive theobject data 115 and/or the location data 115 from the HMI 120, a cloudcomputer, e.g., a computer connected to the network 125 and external tothe vehicle 101, sensors 110, e.g., a camera capturing an image of theobject, the user device 140, etc. The computer 105 can determine thestowage parameter by analyzing the object data 115 and/or location data115, e.g., the computer 105 can determine whether the objects will fitin the vehicle 101 based on the object data 115, e.g., objectcharacteristics, and can predict whether an object will be stowed basedon location data 115. The computer 105 can actuate a vehicle component120 according to the stowage parameter.

The computer 105 can include a machine learning program to predict thestowage parameters of the vehicle 101. The machine learning program maysubstantially continuously monitor the vehicle 101 location and theobjects stowed at the locations of the vehicle 101. In other words, themachine learning program may store object data 115, location data 115,and storage parameters. For example, the machine learning program mayinclude a vehicle activity log that records, at various times, thevehicle 101 location and objects stored in the vehicle 101. The vehicleactivity log may store location data 115 and object data 115, e.g., thevehicle activity log may store historical data 115, e.g., data 115 fromprevious travels, of the vehicle 101. The vehicle activity log can bepopulated by the object data 115 and the location data 115 from the HMI120, e.g., the user could input the object and location data 115, fromthe network 125, e.g., the vehicle activity log may be in communicationwith the network 125 to receive object data 115, the sensors 110, e.g.,a camera could capture an image of the object, from GPS, etc. Themachine learning program can predict the stowage parameters based on thelocation data 115, e.g., the machine learning program can predict thesame stowage parameters identified when the location data 115 indicatesthe vehicle 101 is returning to a previous location. Additionally, themachine learning program can predict the stowage parameters based on theobject data 115, e.g., the machine learning program can predict thestowage parameters based on the object, e.g., the type of object, thesize of the object, etc., being stowed in the vehicle 101.

The computer 105 can predict the stowage parameters based on theapplying rules derived from the machine learning program. For example,when a vehicle 101 is at a location, if stored object data 115 of theobject(s) stored at the location corresponds to the stored object data115, the machine learning program can increase a probability that anobject corresponding to the object data 115 will be stowed at thelocation. Otherwise, the machine learning program can decrease theprobability. The computer 105 can predict the stowage parameters basedon the probability determined by the machine learning program, e.g., thecomputer 105 can predict stored stowage parameters when the probabilityis above a threshold.

FIGS. 2 and 3 illustrate and example vehicle 101. The vehicle 101includes a plurality of stowage elements 122 a, 122 b, 122 c, and 122 d,referred to collectively as stowage elements 122. A stowage element isany component in the vehicle 101 capable of supporting and/or stowing anobject when the vehicle 101 is in transit. For example, a stowageelement 122 a can be can be a swing arm, e.g., to hang objects, astowage element 122 b a shelf, e.g., to support objects, a stowageelement 122 c can be a storage bin, e.g., to enclose objects, and astowage element 122 d can be a seatback when the seat 120 is in thestowage position, e.g., the seatback of the seat 120 can supportobjects. The example of FIG. 2 shows two objects, 215 a, 215 b(collectively, objects 215) supported by stowage elements 122 a, 122 c,respectively. The example of FIG. 3 shows two objects 215 a, 215 bsupported by stowage elements 122 b, 122 d, respectively. The stowageelements 122 can be actuated individually and/or collectively to supporta plurality, e.g., one or more, objects in the vehicle 101. In otherwords, the computer 105 can actuate one or more stowage elements 122based on the object data 115. The computer 105 can determine the stowageelement 122 for each object and can actuate the stowage elements 122 tosupport each object, as described below.

The objects 215 may be any object transportable in the vehicle 101 bythe user. For example, the user may transfer the objects 215 into thevehicle 101 at the current location and transport the objects 215 to thedestination location in the vehicle 101. The objects 215 may be, forexample, luggage, a parcel, a crate, or any other object that the usermay carry on to the vehicle 101. The computer 105 can determinecharacteristics of the objects 215. An object characteristic is anyphysical property of the object 215. For example, object characteristicscan include dimensions of the object, e.g., length, height, width,circumference, etc., as applicable. As another example, a characteristicmay be a mass or weight of the object. As yet another example, acharacteristic can be a physical feature of the object, e.g., hangable,fragile, durable, etc. The computer 105 can determine the object 215 isfragile based on a material type of the object 215, e.g., glass,porcelain, plastic, metal, etc. When the material type of the object 215is at risk of breaking, e.g., glass, porcelain, etc., the computer 105can determine that the object 215 is fragile, and can predict a“fragile” stowage parameter to protect the object 215. For example, thecomputer 105 could store a look-up table or the like specifying a listof object materials, e.g., plastic, glass, cloth, etc., along withstowage parameters associated with each, e.g., plastic could beassociated with a “tough” parameter, and glass could be associated witha “fragile” parameter. The vehicle 101 can include a plurality ofsensors 110, e.g., a vehicle image sensor, etc., that can detectcharacteristics of the object 215, e.g., as described in Table 1 below.Additionally, or alternatively, the user can identify thecharacteristics of the object 215 via the user device 140.

Table 1 illustrates an example source of object data 115 that thecomputer 105 can analyze to determine the characteristics of the object215.

TABLE 1 Object characteristics Source of object data DimensionsReceiving an image of the object via the sensors User input to the HMIReceiving a message via the network Weight Receiving an image of theobject via the sensors User input to the HMI Sensors detecting weight ofobject stowed in the vehicle Receiving a message via the networkPhysical Features (e.g., Receiving an image of the object via materials,surface the sensors features such as loops User input to the HMI forhooks, handles, etc.) Receiving a message via the network

Table 2 illustrates an example set of data, e.g., a look-up table or thelike, that a computer 105 can store to determine stowage elements 122 ina vehicle 101 for an object based on characteristics of the object.

TABLE 2 Object Characteristics Stowage Element Object has handles Swingarm 122a, Shelf 122b, Seatback 122d Object is formed of fragilematerial, Storage bin 122c e.g., glass, porcelain, etc. Object has avolume, e.g., length, width, Shelf 122b, Seatback 122d height, above athreshold Object has a weight above a threshold Seatback 122d

The computer 105 can predict stowage parameters based on object data 115and location data 115. The object data 115 can be a characteristic of anobject 215. The computer 105 can compare the object 215 characteristicsto the list of possible stowage parameters. For example, when an object215 has one or more handles or loops, the computer 105 can determinethat the object 215 is hangable, and can predict a stowage parameter to“hang.” In this situation, the computer 105 can assign the object 215 tothe swing arm 122 a. As another example, when the material type of theobject is at risk of breaking, e.g., glass, porcelain, etc., thecomputer 105 can determine that the object is fragile and can predict astowage parameter to “enclose.” In this situation, the computer 105 canassign the object to the storage bin 122 c. As yet another example, whenthe weight and/or the dimensions of the object 215 are above athreshold, the computer 105 can determine that the object is large,e.g., the object 215 exceeds the carrying capacity of the swing arm 122a and/or the dimensions of the storage bin 122 c, and can predict astowage parameter to “support.” In this situation, the computer 105 canassign the object 215 to the shelf 122 b. Alternatively, the computer105 can assign the object 215 to the seatback 122 d when the seat 120 isin the stowage position, as set forth below. When the computer 105assigns the object 215 to the seatback 122 d, the object 215 may exceedthe carrying capacity and/or the dimensions of the shelf 122 b, i.e.,the weight and/or dimensions of the object 215 may exceed a secondthreshold. The location data 115 can be geo-coordinate data, as setforth above, of the vehicle 101. The computer 105 can associate thelocation data 115 of the vehicle 101 with the object data 115, e.g., thecomputer 105 can identify objects stowed in the vehicle 101 at aspecific location. The computer 105 can store the location data 115 andthe object data 115 such that the computer 105 can predict specificobject data 115 that can be received with specific location data 115,e.g., the computer 105 can predict whether an object will be stowed at alocation.

The computer 105 can predict the stowage parameters based on a messagesent from each sensor in a set of seat sensors 110. As set forth above,the seat sensor 110 can detect whether a user is in the seat 120. Thecomputer 105 can actuate the seat 120 from the seated position to thestowage position. When the seat sensor 110 detects a user in the seat120, the seat sensor 110 can send a message to the computer 105, and thecomputer 105 can retain the seat 120 in the seated position to support auser. Otherwise, the computer 105 can actuate the seat 120 from theseated position, shown in hidden (i.e., dashed) lines in FIG. 3, to thestowage position to support objects 215 stowed in the vehicle 101 cabin.For example, when the dimensions and/or the weight of an object 215 areabove the second threshold, e.g., when the object 215 is larger than ashelf 122 b and/or exceeds the carrying capacity of a shelf 122 b, thecomputer 105 can actuate the seat 120 to the stowage position and assignthe object to the seatback 122 d.

The computer 105 can actuate the stowage elements 122 based on thestowage parameters, e.g., the stowage elements 122 can be associatedwith the stowage parameters. Further for example, the computer 105 canactuate the stowage elements 122 from a first position to a secondposition. For example, each stowage element 122 can include an actuatorto move the stowage element 122 from the first position to the secondposition. The actuator can be any suitable mechanism, such as a motor,e.g., an electric motor, attached to a pivoting rod, a hydrauliccylinder attached to a pivoting rod. In another example, a spring canbias the stowage element 122 to the second position, e.g., a solenoid, alatch, etc., can retain the stowage element 122 in the first positionand the computer 105 can send a message to release the solenoid, thelatch, etc. such that the spring can move the stowage element 122 to thesecond position. The computer 105 can send a message to an actuator tomove the stowage element 122 from the first position to the secondposition, e.g., to open a lid, lower a shelf or hook, etc.

In the first position, the stowage elements 122 can be positioned in thevehicle 101 such that the stowage elements 122 are disposed along theinterior trim, e.g., a carpet, a pillar applique, the seatback, etc., ofthe vehicle 101, as shown in hidden lines in FIG. 2. In the secondposition, the stowage elements 122 can extend into the vehicle 101 cabinsuch that the stowage elements 122 can support one or more objects 215.The computer 105 can predict one stowage parameter for each object of aplurality of objects 215, e.g., the computer 105 can determine acharacteristic of each object 215. In this situation, the computer 105can actuate one or more stowage elements 122 from the first position tothe second position to support each object 215 based on a characteristicof each object 215. In other words, the stowage elements 122 can beadaptable according to the objects 215 stowed in the vehicle 101.

FIG. 4 illustrates an example process 300 for predicting stowageparameters based on a current location and a destination location of thevehicle 101 and actuating a vehicle component 120 based on the stowageparameter. The process 300 begins in a block 305, in which the computer105 determines a current location of the vehicle 101. As describedabove, the computer 105 can determine the current location of thevehicle 101 based on geo-coordinates provided via a navigation system,such as a GPS navigation system.

Next, in a block 310, the computer 105 determines a destination locationof the vehicle 101. As described above, the computer 105 can determinethe destination location of the vehicle 101 based on a message, e.g.,location data 115, received via the HMI 120, the user device 140, thevehicle activity log input to a machine learning program, etc.

In the block 315, the computer 105 determines whether the destinationlocation matches, i.e., specifies a location within a predeterminedthreshold distance of, e.g., 10 meters, 50 meters, 100 meters, etc.,stored location data. In this context, the destination location can“match” the stored location data based on the geo-coordinates in thestored location data and one or more sets of geo-coordinates included inmap data or other data stored in the computer 105 memory (or locationdata 115 can be a street address or some other set of data to specify alocation). The computer 105 memory further typically stores respectivelocation descriptors (e.g., “grocery store,” “mall,” “school,” “home,”“post office,” etc.) associated with respective sets (latitude andlongitude) of geo-coordinates. The location data 115 matches the storedlocation data when the location data 115 is within a threshold distance,e.g., within a radius, of the stored location data. As described above,the computer 105 can store object and/or location data 115 as a vehicleactivity log. When the location data 115 matches the stored locationdata, e.g., the vehicle 101 returns to a previous location based on thegeo-coordinates, an address, etc., a machine learning program can, usingknown techniques, predict object data 115 to be received, e.g., anobject 215 to be stowed, at the location. If the computer 105 determinesthe location data 115 matches stored location data, the process 300continues to a block 320. Otherwise, the process 300 continues to ablock 325.

In the block 320, the computer 105 determines whether the location data115 is associated with stowage parameters; the computer 105 can queryits memory, data store, etc., to determine whether stowage parametersare stored for the location data 115. As described above, the computer105 can store location data 115 and object data 115 associated, e.g.,received, with the location data 115 to predict an object 215 to bestowed in the vehicle 101 at a specific location, e.g., the currentand/or destination location. The computer 105 can predict the stowageparameter when a probability output by the machine learning program isabove a threshold, e.g., the object is likely to be stowed in thevehicle 101 at the current and/or destination location. If the computer105 determines the location data 115 is associated with stowageparameters, the computer 105 can select the stowage parameters, and theprocess 300 continues to a block 340. Otherwise, the process 300continues to a block 325.

In the block 325, the computer 105 predicts whether an object 215 willbe stowed in the vehicle 101 at one of the current location and thedestination location. As described above, the computer 105 can receiveobject data 115 associated with the object 215 to be stowed in thevehicle 101. For example, the computer 105 can query its memory, datastore, etc. to determine whether object data 115 is associated with thelocation data 115 for a user. As another example, the computer 105 canreceive reference data 115, e.g., object data 115 associated with thelocation data 115 for other users, via the network 125 to determine thestowage parameters when object data 115 is not associated with locationdata 115 for the user. In other words, the reference data 115 canidentify objects 215 usually stowed in a vehicle 101 at the location. Inthis situation, the computer 105 can predict the stowage parametersbased on the reference data 115. The computer 105 can analyze the objectdata 115, e.g., the characteristics of the object 215 to determinewhether the object 215 can be stowed, i.e., whether it will fit, in astowage position the vehicle 101. For example, the computer 105 candetermine whether the size, e.g., dimensions, and/or weight of theobject 215 is above a threshold for the vehicle 101. If the computer 105determines the object can be stowed in the vehicle 101, the process 300continues to a block 335. Otherwise, the process 300 continues to ablock 330.

In the block 330, the computer 105 can send a message to the server 130via the network 125 requesting a second vehicle. The message can includethe location data 115, e.g., the current location and the destinationlocation of the user, and the object data 115. The second vehicle can beselected based the object data 115, e.g., the object 215 can fit in thesecond vehicle, and the location data 115, e.g., the location of thesecond vehicle is within a threshold distance, e.g., a radius, from thecurrent location. After the second vehicle is requested, the process 300ends.

In the block 335, the computer 105 predicts the stowage parameters basedon the object data 115. As described above, the computer 105 candetermine a stowage parameter based on the characteristics of the object215 and can assign the object to a stowage element 122 corresponding tothe stowage parameter, e.g., hangable, fragile, heavy, etc.

Next, in a block 340, the computer 105 actuates a vehicle component 120based on the stowage parameters. For example, the computer 105 canactuate the suspension component from the driving position to thestowage position to assist the user in stowing the object 215 in thevehicle 101. As another example, the computer 105 can actuate thestowage elements 122 from the first position to the second position. Asdescribed above, the computer 105 can actuate one or more stowageelements 122 to support one or more objects 215 in the vehicle 101.After the object 215 is stowed in the vehicle 101, the computer 105 canactuate the suspension component from the stowage position to thedriving position, and the process 300 ends.

As used herein, the adverb “substantially” modifying an adjective meansthat a shape, structure, measurement, value, calculation, etc. maydeviate from an exact described geometry, distance, measurement, value,calculation, etc., because of imperfections in materials, machining,manufacturing, data collector measurements, computations, processingtime, communications time, etc.

Computers 105 generally each include instructions executable by one ormore computers such as those identified above, and for carrying outblocks or steps of processes described above. Computer-executableinstructions may be compiled or interpreted from computer programscreated using a variety of programming languages and/or technologies,including, without limitation, and either alone or in combination,Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, aprocessor (e.g., a microprocessor) receives instructions, e.g., from amemory, a computer-readable medium, etc., and executes theseinstructions, thereby performing one or more processes, including one ormore of the processes described herein. Such instructions and other datamay be stored and transmitted using a variety of computer-readablemedia. A file in the computer 105 is generally a collection of datastored on a computer readable medium, such as a storage medium, a randomaccess memory, etc.

A computer-readable medium includes any medium that participates inproviding data (e.g., instructions), which may be read by a computer.Such a medium may take many forms, including, but not limited to,non-volatile media, volatile media, etc. Non-volatile media include, forexample, optical or magnetic disks and other persistent memory. Volatilemedia include dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Common forms of computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any othermemory chip or cartridge, or any other medium from which a computer canread.

With regard to the media, processes, systems, methods, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. For example, in the process 500, oneor more of the steps could be omitted, or the steps could be executed ina different order than shown in FIG. 5. In other words, the descriptionsof systems and/or processes herein are provided for the purpose ofillustrating certain embodiments, and should in no way be construed soas to limit the disclosed subject matter.

Accordingly, it is to be understood that the present disclosure,including the above description and the accompanying figures and belowclaims, is intended to be illustrative and not restrictive. Manyembodiments and applications other than the examples provided would beapparent to those of skill in the art upon reading the abovedescription. The scope of the invention should be determined, not withreference to the above description, but should instead be determinedwith reference to claims appended hereto and/or included in anon-provisional patent application based hereon, along with the fullscope of equivalents to which such claims are entitled. It isanticipated and intended that future developments will occur in the artsdiscussed herein, and that the disclosed systems and methods will beincorporated into such future embodiments. In sum, it should beunderstood that the disclosed subject matter is capable of modificationand variation.

The article “a” modifying a noun should be understood as meaning one ormore unless stated otherwise, or context requires otherwise. The phrase“based on” encompasses being partly or entirely based on.

What is claimed is:
 1. A method, comprising: identifying a currentlocation and a destination location of a vehicle; predicting a stowageparameter based on the current and destination locations; and actuatinga vehicle component based on the stowage parameter.
 2. The method ofclaim 1, wherein the vehicle component is one of a suspension, a seat,and a stowage element.
 3. The method of claim 1, further comprisingpredicting the stowage parameter based on a characteristic of an objectas well as one or both of the current and destination locations in thevehicle.
 4. The method of claim 3, further comprising predicting onestowage parameter for each object of a plurality of objects.
 5. Themethod of claim 1, further comprising receiving a message from a userdevice, and predicting the stowage parameter based on the message fromthe user device.
 6. The method of claim 1, further comprising predictingthe stowage parameter based on a vehicle activity log.
 7. The method ofclaim 1, further comprising predicting the stowage parameter based userinput.
 8. The method of claim 1, further comprising predicting thestowage parameter based on a message from each sensor in a set of a seatsensors.
 9. The method of claim 1, further comprising predicting thestowage parameter by applying rules derived from machine learning.
 10. Asystem, comprising a computer programmed to: identify a current locationand a destination location of a vehicle; predict a stowage parameterbased on the current and destination locations; and actuate a vehiclecomponent based on the stowage parameter.
 11. The system of claim 10,wherein the vehicle component is one of a suspension, a shelf, a hook, astorage bin, and a seat.
 12. The system of claim 10, wherein thecomputer is further programmed to predict the stowage parameter based ona characteristic of an object as well as one or both of the current anddestination locations in the vehicle.
 13. The system of claim 12,wherein the computer is further programmed to predict one stowageparameter for each object of a plurality of objects.
 14. The system ofclaim 10, wherein computer is further programmed to receive a messagefrom a user device and predict the stowage parameter based on themessage from the user device.
 15. The system of claim 10, wherein thecomputer is further programmed to predict the stowage parameter based ona vehicle activity log.
 16. The system of claim 10, wherein the computeris further programmed to predict the stowage parameter based user input.17. The system of claim 10, wherein the computer is further programmedto predict the stowage parameter based on a message from each sensor ina set of a seat sensors.
 18. The system of claim 10, wherein thecomputer is further programmed to predict the stowage parameter byapplying rules derived from machine learning.
 19. A system, comprising:a stowage element that includes an actuator arranged to move at leastpart of the stowage element; and a computer programmed to: identify acurrent location and a destination location of a vehicle; predict astowage parameter based on the current and destination locations; andactuate a vehicle component based on the stowage parameter.
 20. Thesystem of claim 19, wherein the computer is further programmed topredict the stowage parameter based on a characteristic of an object aswell as one or both of the current and destination locations in thevehicle.